Over the past couple of years, I’ve had the privilege to advance my R skills, as well as acquire useful functions that should aid anyone using R for behavioral science. This list is not exhaustive, but a list of my most used functions, packages, and useful tips!
%>% OperatorThe pipe operator, written as %>% takes the output of one function and passes it into another function as an argument. This allows us to link a sequence of analysis steps.
For a mathematical analogy, f(x) can be rewritten as x %>% f
## compute the logarithm of `x`
x <- 1
log(x)## [1] 0## compute the logaritm of `x`
x %>% log()## [1] 0Why is this useful though? R is a functional language, which means that your code often contains a lot of parenthesis, ( and ). When you have complex code, this often will mean that you will have to nest those parentheses together. This makes your R code hard to read and understand.
# Initialize `x`
x <- c(0.109, 0.359, 0.63, 0.996, 0.515, 0.142, 0.017, 0.829, 0.907)
# Compute the logarithm of `x`, return suitably lagged and iterated differences,
# compute the exponential function and round the result
round(exp(diff(log(x))), 1)## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1# Compute the same computation as above but with pipe in operator
x %>% log() %>%
diff() %>%
exp() %>%
round(1)## [1] 3.3 1.8 1.6 0.5 0.3 0.1 48.8 1.1In short, here are four reasons why you should be using pipes in R:
You’ll structure the sequence of your data operations from left to right, as apposed to from inside and out;
You’ll avoid nested function calls;
You’ll minimize the need for local variables and function definitions;
You’ll make it easy to add steps anywhere in the sequence of operations.
By far my most used package in R is dplyr. See documentation here